GPT-2 implemented using graphics shaders

In plain words

This project implements GPT-2, a language model, using WebGL and graphics shaders rather than traditional GPU computing frameworks. It demonstrates an unconventional approach to neural network computation by leveraging shader programming, a technique that predates modern CUDA-based GPU acceleration. The implementation is available on GitHub and runs in web browsers, making it a proof-of-concept for general-purpose GPU programming through graphics APIs.

written from the facts on this page · September 2026

From the sources

A browser-based, WebGL2 implementation of GPT-2 with transform block and attention matrix visualization - nathanrs/gpt2-webgl

pip install torch numpy transformers Run the downloader: python download_weights.py This will fetch: wte.bin (token embeddings) wpe.bin (positional embeddings) c_attn_q_w_0.bin … c_attn_q_w_11.bin c_attn_k_w_0.bin … etc. lm_head_w.bin , lm_head_b.bin And a generated manifest.json mapping names → URLs Front-end Setup with Vite We use Vite to bundle TS, serve ESM modules & handle js-tiktoken : npm install Start the local dev server: npm run dev Open your browser at http://localhost:5173 Any changes under src/ will trigger HMR andfrom github.com

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